{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "file_name = 'lstm_kernel_reg'\n",
    "df = pd.read_csv('result/'+file_name+'.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>l1 0.0000,l2 0.0000</th>\n",
       "      <th>l1 0.1000,l2 0.0000</th>\n",
       "      <th>l1 0.0100,l2 0.0000</th>\n",
       "      <th>l1 0.0010,l2 0.0000</th>\n",
       "      <th>l1 0.0001,l2 0.0000</th>\n",
       "      <th>l1 0.0000,l2 0.1000</th>\n",
       "      <th>l1 0.0000,l2 0.0100</th>\n",
       "      <th>l1 0.0000,l2 0.0010</th>\n",
       "      <th>l1 0.0000,l2 0.0001</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>3.000000e+01</td>\n",
       "      <td>3.000000e+01</td>\n",
       "      <td>3.000000e+01</td>\n",
       "      <td>3.000000e+01</td>\n",
       "      <td>3.000000e+01</td>\n",
       "      <td>3.000000e+01</td>\n",
       "      <td>3.000000e+01</td>\n",
       "      <td>3.000000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>133487.856011</td>\n",
       "      <td>4.366153e+07</td>\n",
       "      <td>4.360784e+07</td>\n",
       "      <td>4.367009e+07</td>\n",
       "      <td>6.248348e+05</td>\n",
       "      <td>4.369478e+07</td>\n",
       "      <td>7.566219e+07</td>\n",
       "      <td>2.878313e+06</td>\n",
       "      <td>1.050728e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>124801.829503</td>\n",
       "      <td>3.627226e+05</td>\n",
       "      <td>3.620032e+05</td>\n",
       "      <td>4.089471e+05</td>\n",
       "      <td>6.901334e+05</td>\n",
       "      <td>4.083946e+05</td>\n",
       "      <td>1.271405e+08</td>\n",
       "      <td>2.529282e+06</td>\n",
       "      <td>2.446572e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18434.928927</td>\n",
       "      <td>4.304834e+07</td>\n",
       "      <td>4.290940e+07</td>\n",
       "      <td>4.273714e+07</td>\n",
       "      <td>5.018981e+04</td>\n",
       "      <td>4.301423e+07</td>\n",
       "      <td>6.646785e+05</td>\n",
       "      <td>9.849185e+04</td>\n",
       "      <td>2.740524e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47194.081932</td>\n",
       "      <td>4.331944e+07</td>\n",
       "      <td>4.339696e+07</td>\n",
       "      <td>4.351494e+07</td>\n",
       "      <td>1.397884e+05</td>\n",
       "      <td>4.340562e+07</td>\n",
       "      <td>4.155738e+07</td>\n",
       "      <td>1.028379e+06</td>\n",
       "      <td>1.139312e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>94750.483247</td>\n",
       "      <td>4.370499e+07</td>\n",
       "      <td>4.361193e+07</td>\n",
       "      <td>4.365038e+07</td>\n",
       "      <td>4.048155e+05</td>\n",
       "      <td>4.375185e+07</td>\n",
       "      <td>4.321990e+07</td>\n",
       "      <td>1.783012e+06</td>\n",
       "      <td>4.133968e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>184617.641298</td>\n",
       "      <td>4.385875e+07</td>\n",
       "      <td>4.377604e+07</td>\n",
       "      <td>4.407423e+07</td>\n",
       "      <td>7.889972e+05</td>\n",
       "      <td>4.395016e+07</td>\n",
       "      <td>4.380739e+07</td>\n",
       "      <td>4.423668e+06</td>\n",
       "      <td>1.006873e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>501912.458928</td>\n",
       "      <td>4.431846e+07</td>\n",
       "      <td>4.449379e+07</td>\n",
       "      <td>4.426704e+07</td>\n",
       "      <td>2.440510e+06</td>\n",
       "      <td>4.453783e+07</td>\n",
       "      <td>5.033895e+08</td>\n",
       "      <td>9.283874e+06</td>\n",
       "      <td>1.344800e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            l1 0.0000,l2 0.0000  l1 0.1000,l2 0.0000  l1 0.0100,l2 0.0000  \\\n",
       "Unnamed: 0                                                                  \n",
       "count                 30.000000         3.000000e+01         3.000000e+01   \n",
       "mean              133487.856011         4.366153e+07         4.360784e+07   \n",
       "std               124801.829503         3.627226e+05         3.620032e+05   \n",
       "min                18434.928927         4.304834e+07         4.290940e+07   \n",
       "25%                47194.081932         4.331944e+07         4.339696e+07   \n",
       "50%                94750.483247         4.370499e+07         4.361193e+07   \n",
       "75%               184617.641298         4.385875e+07         4.377604e+07   \n",
       "max               501912.458928         4.431846e+07         4.449379e+07   \n",
       "\n",
       "            l1 0.0010,l2 0.0000  l1 0.0001,l2 0.0000  l1 0.0000,l2 0.1000  \\\n",
       "Unnamed: 0                                                                  \n",
       "count              3.000000e+01         3.000000e+01         3.000000e+01   \n",
       "mean               4.367009e+07         6.248348e+05         4.369478e+07   \n",
       "std                4.089471e+05         6.901334e+05         4.083946e+05   \n",
       "min                4.273714e+07         5.018981e+04         4.301423e+07   \n",
       "25%                4.351494e+07         1.397884e+05         4.340562e+07   \n",
       "50%                4.365038e+07         4.048155e+05         4.375185e+07   \n",
       "75%                4.407423e+07         7.889972e+05         4.395016e+07   \n",
       "max                4.426704e+07         2.440510e+06         4.453783e+07   \n",
       "\n",
       "            l1 0.0000,l2 0.0100  l1 0.0000,l2 0.0010  l1 0.0000,l2 0.0001  \n",
       "Unnamed: 0                                                                 \n",
       "count              3.000000e+01         3.000000e+01         3.000000e+01  \n",
       "mean               7.566219e+07         2.878313e+06         1.050728e+06  \n",
       "std                1.271405e+08         2.529282e+06         2.446572e+06  \n",
       "min                6.646785e+05         9.849185e+04         2.740524e+04  \n",
       "25%                4.155738e+07         1.028379e+06         1.139312e+05  \n",
       "50%                4.321990e+07         1.783012e+06         4.133968e+05  \n",
       "75%                4.380739e+07         4.423668e+06         1.006873e+06  \n",
       "max                5.033895e+08         9.283874e+06         1.344800e+07  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.set_index(df.columns[0])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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5fv161tfXs7i4mKWlpaFLg0HtakHq1tptV+ZqrT2f5PmpVgQAAAB74LVFpy9cuJDNzc3M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\nF2HNmcg3xx8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc6dae82510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(20,10))\n",
    "df.boxplot()\n",
    "plt.yscale('log')\n",
    "plt.savefig('result/'+file_name+'.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
